Knowledge diffusion and convergence in open learning environments as a social contagion

ABSTRACT

Online learning has become increasingly prevalent, offering accessibility and flexibility in education. Learners often engage in discussions and share ideas through posts and blogs, creating a form of "group wisdom" that generates and transforms new knowledge. Thus, this study examines how knowledge diffuses in online learning environments using a contagion model. Factors such as cohesion (familiarity relationships) and structural equivalence (similar network positions) that influence this process were tested. By analyzing 683 blog entries from 50 participants in a five-week higher education online course, we employed a word vector approach to create a knowledge similarity network.Our findings indicate that knowledge diffusion occurs through social contagion, leading to knowledge convergence. Notably, structural equivalence plays a more significant role in this process than cohesion. This research provides new insights into the social dynamics mechanism of knowledge diffusion in online learning environments.

KEYWORDS:

• Knowledge Diffusion
• Knowledge Convergence
• Open Learning Environments

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Xiaojie Niu

Xiaojie Niu is a PhD student at Beijing Normal University. She received her BS and MA degree in Beijing Normal University. Her research interests are learning analysis, learning science, social network in online learning and complex systems.

Jingjing Zhang

Jingjing Zhang is Professor and directs the Big Data Centre for Technology-mediated Education at Beijing Normal University (BNU), China. She received her BSc in Computer Science from BNU, an MRes from University College London (UCL), an MSc and a DPhil from the University of Oxford. Dr. Zhang’s current work focuses on developing data mining techniques (e.g. network analysis) to explore human relationships and activities online, particularly in the learning sciences. For more information, please visit www.researchgate.net/profile/Jingjing_Zhang23.